Chen Chun-Rong, Jin Hong-Liang, Xu Qian-Jie, Yuan Yu-Liang, Hu Zu-Hai, Liu Ya, Lei Hai-Ke
Department of Health Information Management, School of Public Health and Management, Chongqing Three Gorges Medical and Pharmaceutical College, Chongqing 404120, China.
Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing 400030, China.
World J Gastrointest Oncol. 2025 Jun 15;17(6):105790. doi: 10.4251/wjgo.v17.i6.105790.
Few studies have specifically modeled the risk of venous thromboembolism (VTE) for postoperative hepatocellular carcinoma (HCC) patients, although HCC is the third leading cause of cancer death worldwide. This study aimed to develop and validate a nomogram that accurately predicts the risk of VTE in patients after HCC surgery.
To develop and validate a nomogram to accurately predict the risk of VTE in postoperative HCC patients by integrating clinical and laboratory risk factors. The model seeks to provide a user-friendly tool for identifying high-risk individuals who may benefit from targeted anticoagulation therapy, thereby improving clinical decision-making and patient outcomes.
Data from patients who underwent HCC surgery at Chongqing University Cancer Hospital in China were analyzed. Through univariate and multivariate logistic regression analyses, independent risk factors for VTE were identified and integrated into a nomogram. The predictive performance of the nomogram was assessed receiver operating characteristic curves, calibration curves, decision curve analysis and other relevant metrics.
Of 905 postoperative HCC patients were included in the study. The nomogram incorporated eight independent risk factors for VTE: Karnofsky Performance Scale, base disease, cancer stage (tumor-node-metastasis), chemotherapy, D-dimer concentration, white blood cell count, hemoglobin, and fibrinogen. The C-index for the nomogram model was 0.825 in the training cohort and 0.820 in the validation cohort, indicating good discriminative ability. Calibration plots of the model revealed high concordance between the predicted probabilities and observed outcomes.
We developed and validated a novel nomogram that can accurately estimate the risk of VTE in individual postoperative HCC patients. This model can identify high-risk patients who may benefit from targeted anticoagulation therapy.
尽管肝细胞癌(HCC)是全球癌症死亡的第三大主要原因,但很少有研究专门对术后肝细胞癌患者的静脉血栓栓塞(VTE)风险进行建模。本研究旨在开发并验证一种能准确预测肝癌手术后患者VTE风险的列线图。
通过整合临床和实验室风险因素,开发并验证一种列线图,以准确预测肝癌术后患者的VTE风险。该模型旨在提供一种用户友好的工具,用于识别可能从靶向抗凝治疗中获益的高危个体,从而改善临床决策和患者预后。
对在中国重庆大学附属肿瘤医院接受肝癌手术的患者数据进行分析。通过单因素和多因素逻辑回归分析,确定VTE的独立危险因素并将其整合到列线图中。采用受试者工作特征曲线、校准曲线、决策曲线分析等相关指标评估列线图的预测性能。
本研究纳入了905例肝癌术后患者。该列线图纳入了八个VTE的独立危险因素:卡氏功能状态评分、基础疾病、癌症分期(肿瘤-淋巴结-转移)、化疗、D-二聚体浓度、白细胞计数、血红蛋白和纤维蛋白原。列线图模型在训练队列中的C指数为0.825,在验证队列中的C指数为0.820,表明具有良好的辨别能力。模型的校准图显示预测概率与观察结果之间具有高度一致性。
我们开发并验证了一种新型列线图,可准确估计个体肝癌术后患者的VTE风险。该模型可以识别可能从靶向抗凝治疗中获益的高危患者。